Enforcement Selection Skill

To configure quality gates and enforcement policies for AI agents, you can select enforcement categories like Quality, Safety, and Integrity to generate an enforcement.yaml file. This configuration includes threshold settings, override policies, and CI/CD integration hooks.

An enforcement.yaml file is a configuration document that codifies governance principles into operational guardrails for AI systems. It works with CI/CD governance by defining integration hooks, threshold configurations, and override policies that automatically enforce values during project execution.

You can generate an enforcement configuration from onboarding principles by running an enforcement-selection workflow. This process guides you through choosing appropriate enforcement categories based on risk alignment and outputs a structured enforcement.yaml file.

Yes, you can use enforcement patterns to ensure agent safety and integrity checks by selecting the Safety and Integrity enforcement categories. These patterns transform abstract principles into operational guardrails with specific thresholds and override policies.

Yes, the enforcement selection process fully supports policy configuration for override thresholds. It satisfies functional requirements for threshold configuration and override policies within a YAML-based governance workflow, allowing teams to customize enforcement strictness.

A limitation of using a YAML-based governance workflow for enforcement is that it relies on predefined threshold configurations and override policies, which may require manual updates as project risks evolve. The enforcement.yaml must be maintained across projects to remain effective.